Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization
With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is difficult to adapt to the complex and changeable marine environment. Therefore, based on the RT-DETR model of transformer architecture, an improved scheme for maritime distress target detection is proposed to improve the small target recognition ability and detection efficiency. Specific improvements include: a small target-focused convolution module (SFConv) is designed to enhance the efficiency of feature extraction and reasoning of small-scale targets; The cross-scale feature interaction optimization module (SPE) is further proposed to improve the ability of multi-scale perception and background suppression; The Focaler-DIoU loss function is introduced to enhance the discrimination performance of the model for difficult samples. On the basis of maintaining the end-to-end detection advantage of RT-DETR, the improvement is of 0.83474, which is 5.7% higher than the original model (0.78964). The accuracy and robustness of the model in complex marine environment is significantly improved, and technical support is provided for the construction of an efficient and intelligent marine monitoring and emergency response system.
Authors
- Jian Xu (ORCID: https://orcid.org/0000-0002-1752-6557)
- Y J Zhang (ORCID: https://orcid.org/0000-0003-1167-4716)
- Z Liu
- Kun Liu (ORCID: https://orcid.org/0000-0002-4764-030X)
- Ying Liu (ORCID: https://orcid.org/0000-0001-8931-9656)
- Xinbo Chang
Institutions
- Qingdao University (CN)
- Harbin Engineering University (CN)
- Harbin Institute of Technology (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2026-06-09
- DOI
- https://doi.org/10.3390/rs18121908
- Primary Topic
- Oil Spill Detection and Mitigation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hainan Province
- Natural Science Foundation of Heilongjiang Province
- Natural Science Foundation of Shandong Province